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Transcript of Hedge Funds and the Technology markus/research/papers/hedgefunds_bubble_slidHedge Funds and the...

  • 1

    Hedge Funds and the Technology Bubble

    Markus K. BrunnermeierPrinceton University

    Stefan NagelLondon Business School

  • 2

    The Technology Bubble

    Ofek-Richardson (2001WP): Valuation of Internet stocks implied Average expected earnings growth of about 3,000% in ten years,

    assuming that they already achieved old economy profit margins Zero percent cost of capital for ten years

    A price bubble? We assume so and try to understand it.

    Chart (Jan. 98 - Dec. 00)38 day average

    NASDAQ Combined Composite Index

  • 3

    Bubbles and Rational Speculative Activity

    Efficient markets viewIf there are many sophisticated traders in the market, they may cause

    these bubbles to burst before they really get under way(Fama 1965)

    Limits to arbitrage?Had I followed my own advice, I would have lost my shirt everybody

    knew that it could not go on like this. The start and end of a bubble just cannot be explained rationally.

    (Milton Friedman, 2001)

    So, you think that investors are irrationalbut then you expect them to become rational just when you have gone short?

    (A hedge fund managers wife, 1999)

    How did sophisticated investors react to the bubble?

  • 4

    Why Did Rational Speculation Fail to Prevent the Bubble ?

    (1) Unawareness of the bubble?

    Implication: Rational speculators would perform as badly as other investors when prices collapse

    (2) Limits to arbitrage?

    Reluctance to trade against mispricing Fundamental risk (Wurgler-Zhurvaskaya 2002) Noise trader risk and myopia (DSSW 1990a; Dow-Gorton 1994) Liquidation risk (Shleifer-Vishny 1997) Synchronization risk (Abreu-Brunnermeier 2002; 2003) Short-sales constraints (Ofek-Richardson 2003; Cochrane 2002)

    Implication: Rational speculators may be reluctant to go short in overpriced Tech stocks

  • 5

    Why Did Rational Speculation Fail to Prevent the Bubble ?

    (3) Predictable investor sentiment

    Incentives for rational arbitrageurs to ride a bubble Predictable bubble growth (Abreu-Brunnermeier 2003) Anticipation of positive-feedback trader demand (DSSW 1990b)

    Implication: Rational speculators may want to hold Tech stocks and try to go short before prices collapse

  • 6

    Hedge Funds

    We look at positions held by hedge funds

    Why hedge funds?

    Hedge funds are able to go short

    Managers have high-powered incentives

    Lock-in periods

    Hedge Funds come close to the ideal of rational speculators

  • 7

    Outline & Preview

    1. Related Empirical Literature

    2. Data and Methodology

    3. Empirical Results

    On balance, hedge funds had significant long exposure to technology stocks they were riding the bubble

    Hedge Funds skillfully anticipated the downturn on a stock-by-stock level

    4. Conclusions

  • 8

    Related Empirical Literature

    Technology Bubble

    Ofek-Richardson (2003), Lamont-Thaler (2003), Cochrane (2002)

    Limits to Arbitrage

    Pontiff (1996), Mitchell-Pulvino-Stafford (2002), Baker-Savasoglu (2002), Wurgler-Zhuravskaya (2002)

    Hedge Funds

    Skills and risks: Fung-Hsieh (1997), Ackermann et al. (1999), Agarwal-Naik (2000)

    Role in financial crises: Brown-Goetzmann-Park (2000), Fung-Hsieh (2000)

  • 9

    Data

    Hedge fund stock holdings 1998 - 2000

    Quarterly 13F SEC filings from the CDA/Spectrum Database

    Filing of 13F is mandatory for all institutional investors

    With holdings in U.S. stocks of more than $100 million Domestic and foreign

    Holdings reported at the manager level, not at the fund level

    Example 1: Holdings aggregated for Soros Fund Management, without a break-up for the Quantum Fund and other Soros-funds

    Example 2: Holdings for Montgomery Asset Management dominated by its large mutual funds/investment advisory activities. Discarded.

    No short positions

  • 10

    Data

    Identification of hedge fund managers

    Hedge Fund Research Inc. (HFR) Money Manager Directory 1997

    Barrons Feb. 1996

    List of large hedge fund managers in Cottier (1997)

    Pre-sample period sources to avoid survivor bias

  • 11

    Data

    Sample size

    Initial list of 71 managers with CDA/Spectrum data

    18 managers are discarded because a large mutual fund/investmentadvisory business dominates their reported stock holdings

    If registered as investment adviser with the SEC and registration documents (Form ADV) indicate large non-hedge business

    Final sample: 53 managers

    Includes Soros, Tiger, Tudor, D.E. Shaw, and other well-known managers

  • 12

    Data

    Hedge Fund Performance Data 1998 - 2000

    Returns on HFR hedge fund style indexes

    Returns on individual hedge funds managed by the five managers with the largest stock holdings in our sample

    Soros, Tiger, Husic, Omega, Zweig-DiMenna

    Stock Returns and Accounting Data

    CRSP/COMPUSTAT Merged Database

  • 13

    Aggregate

    Number Mean Median s.i.q.r. Mean Median s.i.q.r. Mean Median s.i.q.r. Stock Hldgs.

    Year Qtr. of Mgrs. ($ mill) ($ mill) ($ mill) (ann.) (ann.) (ann.) ($ mill)

    1998 1 35 1280 295 755 150 56 77 44,794

    2 42 1053 231 445 113 50 49 1.02 0.94 0.34 44,234

    3 42 728 145 364 71 44 30 0.83 0.57 0.40 30,594

    4 41 925 178 417 66 39 36 1.16 1.05 0.58 37,912

    1999 1 39 1070 216 538 74 47 39 0.98 0.84 0.55 41,742

    2 42 995 211 382 75 48 38 1.12 1.12 0.50 41,807

    3 43 927 244 426 69 37 42 1.28 1.32 0.46 39,879

    4 44 1136 270 615 83 46 41 1.02 0.95 0.51 49,981

    2000 1 43 1138 316 792 85 39 49 1.33 1.12 0.71 48,933

    2 44 772 246 383 67 37 41 1.19 0.99 0.75 33,988

    3 45 861 269 413 80 37 34 1.21 1.22 0.63 38,747

    4 48 812 190 427 100 45 37 1.06 0.77 0.70 38,989

    Stock Holdings per Mgr. Portfolio TurnoverNo. of Stocks per Mgr.

    Table ISummary Statistics

  • 14

    Definition of the Bubble Segment

    We look at NASDAQ stocks with high lagged Price/Sales (P/S) ratios

    Advantage over Price/Book, Price/Earnings:

    P/B and P/E can be negative for both distressed Old Economy and high-flying New Economy stocks.

    Sales are always positive even for internet stocks!

    Focus on NASDAQ stocks above the 80th P/S percentile

    Largely identical with technology segment: Contains virtually all dot-coms, Cisco, Sun, EMC etc.

  • 15

    Figure 1: Returns for NASDAQ price/sales quintile portfolios 1998-2000.

    0

    1

    2

    3

    4

    5

    Jan-98 Apr-98 Jul-98 Oct-98 Jan-99 Apr-99 Jul-99 Oct-99 Jan-00 Apr-00 Jul-00 Oct-00

    Total return index

    High P/S Mid P/S Low P/S

  • 16

    Outline

    1. Related Empirical Literature

    2. Data and Methodology

    3. Empirical Results

    Did hedge funds ride the bubble? Stock holdings Factor exposure Positions of individual managers

    Did they successfully time their exposure to technology stocks? Performance of individual stock holdings

    4. Conclusions

  • 17

    0.00

    0.05

    0.10

    0.15

    0.20

    0.25

    0.30

    0.35

    Mar-98 Jun-98 Sep-98 Dec-98 Mar-99 Jun-99 Sep-99 Dec-99 Mar-00 Jun-00 Sep-00 Dec-00

    Hedge Fund Portfolio Market Portfolio

    Proportion invested in NASDAQ high P/S stocks NASDAQ Peak

    Figure 2: Weight of NASDAQ technology stocks (high P/S) in aggregate hedge fund portfolio versus weight in market portfolio

  • 18

    Assessing Short Positions: Factor Model

    Simple model of hedge fund returns: linear combination of asset class returns

    RMt : Market Return RTt RMt : Return on a hypothetical technology hedge fund

    Long tech stocks, short the market et : idiosyncratic return

    Estimation of b and g by OLS

    tMtTtMtt e)R(R gbRR ++=

    tMtTtMtt )R(RRR +++=

  • 19

    Assessing Short Positions: Backing out Portfolio Weights

    (a) In Figure 2 before:

    (b) Taking account of short positions

    (mT = 0.15, average weight of tech stocks in the market portfolio, Fig. 2)

    More comparable to Fig. 2 (set = 1):

    ( )

    m-1m holdingsstock net holdingsTech net w TT2

    +==

    holdingsstock longholdingsTech longw0 =

    )m-(1mholdingsstock long

    holdingsnet tech w TT1 +==

  • 20

    Assessing Short Positions: Hedge Fund Returns

    Monthly returns (net-of-fees) on hedge fund indexes

    Returns on funds of five largest largest managers in our holdings sample (equal-weighted index)

    HFR hedge fund performance indexes for style groups

  • 21

    Table IIExposure of Hedge Funds to the Technology Segment: Two-Factor Return Regressions

    w1 w2

    Index (Market) (TECH) adj. R2 (rel. to agg. long) (rel. to agg. net)

    Large 0.42 0.17 0.56 0.29 0.49(3.51) (2.51) [0.06] [ ]

    Equity-Hedge 0.45 0.15 0.80 0.28 0.44(6.36) (3.92) [0.03] [ ]

    Equity Non-Hedge 0.74 0.16 0.86 0.29 0.34(9.07) (3.57) [0.04] [ ]

    Equity Market-Neutral 0.07 0.01 0.10 0.16 0.32(1.54) (0.53) [0.02] [ ]

    Market Timing 0.25 0.07 0.48 0.21 0.38(3.45) (1.67) [0.03] [ ]

    Short-Selling Specialists -1.00 -0.43 0.80 * -0.52(-5.93) (-4.57) [ ]

    Macro 0.13 0.09 0.34 0.23 0.70(1.84) (2.13) [0.03] [ ]

    Sector Technology 0.71 0.57 0.86 0.64 0.84(5.29) (7.62) [0.06] [ ]

    Panel A: Equal-weighted index of largest funds in our sample (1998-2000)

    Panel B: HFR hedge fund style indexes (1998-2000)

    Factor Loadings Implied weight of Tech stocks

  • 22

    Assessing Short Positions

    Some example calculations

  • 23

    Time